Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligencenpx agentmods add skills/agentii-ai/agentii-investment-intelligence/synthesizeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/synthesize)<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/synthesize"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/synthesize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentii-ai/agentii-investment-intelligence/synthesize"><img src="https://agentmods.dev/badge/skills/agentii-ai/agentii-investment-intelligence/synthesize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00134 | $0.02363 |
| Opus 5 | $0.00067 | $0.01182 |
| Sonnet 5 | $0.00027 | $0.00473 |
| Haiku 4.5 | $0.00013 | $0.00236 |
Grade A, and why
synthesize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agentii.synthesize
The single-point HTML generation step (spec 046 Q46–Q50): ONE thesis-report.html
per thesis, authored from the markdown artifacts and then optimized against
real renders. Analysis skills emit markdown only (Q49); the report is assembled
here, at the synthesis step, after the cross-stock synthesis
(_cross/*_synthesis.md) exists.
When to run
- After the synthesis tasks complete (the
_cross/deliverable is written). - When
convergeemits anhtml_stalefinding (sources or template moved). - On explicit request, or once at thesis completion (Q50 regeneration triggers).
The loop (four steps + optimize)
cd agentii-investment-intelligence
# 1. PACK — deterministic bundle of every source, verbatim (no timestamps),
# PLUS report/metrics.json (per-ticker key_metrics/conclusions/counts —
# machine-ready numbers for KPI tiles and kpi_trend charts, RAW values):
python3 scripts/synthesize_report.py pack --thesis <theses/{nnn}-{slug}>
# 2. AUTHOR — read <thesis>/report-input.md (verbatim sources + citations)
# and <thesis>/report/metrics.json (numbers). Write
# <thesis>/report/content.html — the report's actual content is YOUR judgment.
# 3. ASSEMBLE — validate + inject + gate (advisories on stderr are guidance,
# the hard gates are silent until they fail):
python3 scripts/synthesize_report.py assemble --thesis <thesis-dir> --check-only # fit loop
python3 scripts/synthesize_report.py assemble --thesis <thesis-dir> # deliver
# 4. RENDER — Chrome headless → letter PDF → per-page PNGs + manifest:
python3 scripts/render_report.py render --thesis <thesis-dir>
# 5. OPTIMIZE (required, not optional): READ the PNGs page by page, in batches
# of 3–4. Fix real problems the estimator cannot see — clipped tables, ugly
# URL wrapping, weak density, orphan headings, oversized tiles. Edit
# content.html → re-assemble → re-render until EVERY page is visually clean.
Renders never gate CI (Chrome/poppler may be absent) — the author's own visual
pass is the gate. assemble --check-only remains the estimator fallback; a
missing render is a hard stop for delivery, not a silent skip. render --verify
also checks each PNG is letter-width at the requested dpi.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 165 lines · 134 tokens per session scan A 5219cf4b0630
synthesize is a skill published in the GitHub repository agentii-ai/agentii-investment-intelligence (204 stars, last pushed today), licensed Apache-2.0. It adds 134 tokens to every session and 2,363 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.
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